SC3: consensus clustering of single-cell RNA-seq data.
basic_science · Level V
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- Record sourced from PubMed, PMID 28346451.
- Also identified by DOI 10.1038/nmeth.4236 and PMC identifier 5410170.
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Abstract
Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.
Medical subject headings
- Gene Expression Profiling
- High-Throughput Nucleotide Sequencing
- Sequence Analysis, RNA
- Single-Cell Analysis